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Record W4385802130 · doi:10.3389/fresc.2023.1241981

Cognitive orientation to daily occupational performance (CO-OP) approach as telehealth for a child with developmental coordination disorder: a case report

2023· article· en· W4385802130 on OpenAlexaboutno aff
Hiroyasu Shiozu, Shigeki Kurasawa

Bibliographic record

VenueFrontiers in Rehabilitation Sciences · 2023
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsTelehealthOccupational therapyPsychological interventionPsychologyIntervention (counseling)CognitionRating scaleApplied psychologyOrientation (vector space)Physical therapyClinical psychologyTelemedicineMedicineDevelopmental psychologyPsychiatryHealth care

Abstract

fetched live from OpenAlex

Aim: This study aimed to propose a possible interventional form of occupational therapy through a case study report of the applied Cognitive Orientation to daily Occupational Performance (CO-OP) approach as telehealth for a child with developmental coordination disorder (DCD). Methods: The intervention method was CO-OP-based tele-occupational therapy for a boy with DCD and his mother; 10 sessions were conducted using a video-conferencing system. This study used the Canadian Occupational Performance Measure (COPM) and the Performance Quality Rating Scale (PQRS) as assessment tools. The PQRS evaluated each occupational performance based on videos recorded during the online sessions and videos taken by the mother of the child. Results: The CO-OP approach improved COPM performance and satisfaction as well as PQRS scores in the following five goals: (1) handwriting, (2) column addition, (3) jumping rope, (4) playing on the bar, and (5) riding a bicycle. Conclusions: An online approach based on the CO-OP was realistic and effective, to some extent. Continuing to develop telehealth interventions in the future is recommended.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.340
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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